Foreign Direct Investment, Trade Openness, Financial Development, and Economic Growth Dynamics: Empirical Evidence from Bangladesh

This study investigates the relationships between Foreign Direct Investment (FDI), trade openness, financial development, and economic growth within the context of Bangladesh, a rapidly developing economy in South Asia. By analyzing annual data from 1999 to 2023, the Vector Error Correction Model (VECM) has been used to assess both the short-term dynamics and long-term interactions among these variables. The analysis demonstrates how financial development can enhance the effects of FDI and trade openness, promoting economic growth. The results indicate that while FDI and trade openness directly contribute to economic growth through increased productivity and improved market access, their impacts are significantly bolstered by a well-developed financial sector that supports effective resource allocation and investment. Granger causality tests confirm that financial development causally influences economic growth, highlighting its critical role as a driver of economic performance. The results highlight the need for strategic financial policies that promote financial development to maximize the economic benefits of increased FDI and trade openness.

Introduction

The relationships between foreign direct investment (FDI), trade openness, financial development, and economic growth have long been central topics in theoretical and empirical economic research. Among these factors, financial development is a key factor that helps effectively use foreign direct investment and maximize the advantages of trade openness. A strong financial system with good banking, access to credit, and various financial tools helps allocate resources well, boost investments, and support businesses, thereby driving economic growth (Mashrur & Tabassum, 2023). Bangladesh is a rapidly growing economy in South Asia, making it an interesting case for studying these relationships. In the past two decades, Bangladesh has attracted more FDI, opened its trade policies, and made progress in its financial sector. These changes have contributed to Bangladesh’s economic growth. However, how these factors work together to influence economic growth in Bangladesh is not yet fully understood.

Many studies have shown that FDI can boost growth by improving productivity and increasing exports (e.g., Alfaroet al., 2004; Borenszteinet al., 1998). Trade openness is also seen as a growth driver since it helps countries gain access to new ideas and larger markets (e.g., Dollar & Kraay, 2003; Frankel & Romer, 2017). Financial development is equally important, as it helps channel resources to the right areas, supporting economic activities (e.g., King & Levine, 1993). Despite these insights, there is little research on how FDI, trade openness, and financial development interact specifically in Bangladesh. This paper examines the interaction between Foreign Direct Investment (FDI), trade openness, and financial development, and their collective impact on economic growth in Bangladesh. It specifically explores how financial development can maximize the benefits derived from FDI and trade openness to promote economic growth. The study seeks to answer the following research questions: How do these factors individually and collectively impact economic growth? What are the mechanisms through which financial development enhances the effects of FDI and trade openness in the context of a developing economy like Bangladesh? To address these questions, the study employs a Vector Error Correction Model (VECM) to analyze the long-term and short-term relationships among the variables. This methodological approach allows for an examination of how temporary deviations from equilibrium are corrected over time through adjustments in the variables. The analysis will utilize annual data spanning from 1999 to 2023, focusing on GDP growth as the primary indicator of economic health, supplemented by metrics of FDI, trade openness, and financial development.

Understanding the nexus of FDI, trade openness, and financial development offers critical insights for policymakers. The findings are expected to provide empirical evidence that can guide strategic decisions to optimize the economic benefits of globalization. By identifying effective strategies for financial development that enhance the impacts of FDI and trade openness, this study aims to contribute to sustainable economic growth strategies in Bangladesh and similar developing economies.

Literature Review

Numerous studies have highlighted the positive impact of FDI on economic growth. For example, Borenszteinet al. (1998) found that FDI enhances economic growth by transferring technology and fostering knowledge spillovers in developing countries. They argued that the growth impact is stronger when the host country has a minimum threshold of human capital, which helps in absorbing new technologies brought in by foreign investors.

Alfaroet al. (2004) explored the channels through which FDI influences economic growth and emphasized that sectors like manufacturing and services benefit more from FDI due to technology transfer. They also highlighted that the effectiveness of FDI in promoting growth depends heavily on the development of local financial markets.

Investigating the impact of trade openness on growth in developing countries, Edwards (1998) found that countries with open trade regimes experience higher growth rates compared to those with restrictive trade policies. The study emphasized the importance of implementing complementary policies to maximize the benefits of trade liberalization.

King and Levine (1993) demonstrated that financial development plays a critical role in economic growth by efficiently allocating resources to their most productive uses. Their study showed that countries with more developed financial systems tend to grow faster because such systems support higher levels of investment and entrepreneurship.

The relationship between FDI and financial market development was examined by Hermes and Lensink (2003). The authors showed that FDI has a more significant impact on economic growth when the host country’s financial sector is well developed. They suggested that a robust financial sector enhances the efficiency with which foreign capital is utilized, thereby increasing the productivity of FDI inflows.

Zhang (2001) examined the dynamic relationship between trade openness and FDI, noting that greater openness to trade often leads to increased FDI inflows. The study found that trade liberalization creates a more attractive environment for foreign investors by providing better market access and reducing barriers to business operations.

Li and Liu (2005) analyzed the long-term relationship between trade openness and FDI, finding that increased trade openness leads to higher levels of FDI over time, particularly in countries with stable economic policies. They argued that trade acts as a channel through which foreign firms evaluate potential investment opportunities in a host country.

Levine (1997) emphasized that financial development facilitates economic growth by improving information about investment opportunities, mobilizing savings, and providing better risk management tools. The study found that economies with better-developed financial systems tend to attract more FDI, which in turn supports growth.

A study by Asiedu (2002) examined the determinants of FDI in sub-Saharan Africa and found that countries with open trade policies and developed financial markets were more successful in attracting FDI. The study suggested that policy reforms aimed at liberalizing trade and strengthening financial institutions could help boost FDI inflows and economic growth in the region.

The analysis by Balasubramanyamet al. (1996) highlighted the impact of FDI on economic growth in Asian countries and highlighted that export-oriented economies benefit more from FDI compared to those with inward-looking policies. Their study suggested that FDI can enhance the growth of export sectors by providing access to global markets and advanced production techniques.

Carkovic and Levine (2005) examined the effects of FDI on productivity growth and found that while FDI can positively influence productivity, the extent of its impact depends on the absorptive capacity of the host country, including factors like human capital and the development of local industries.

Mashrur (2024) analyzed the role of digital financial inclusion (DFI) in driving economic growth across Bangladesh, Pakistan, and Nepal, using a Digital Financial Inclusion Index and GMM modeling on data from 2011–2022. The study included foreign direct investment (FDI), government expenditure, trade openness, inflation, and lagged GDP per capita as control variables in the model to account for their influence on economic growth and to provide a comprehensive analysis of contributing factors. The study finds that DFI significantly enhances GDP per capita by reducing transaction costs, increasing financial accessibility, and integrating underserved populations into formal financial systems.

Prasadet al. (2003) explored the relationship between financial integration and economic growth, arguing that countries more financially integrated with the global economy tend to experience higher growth rates. Their study found that financial openness and sound macroeconomic policies attract more FDI and stimulate economic development.

Aghionet al. (2005) found that financial development enhances the benefits of trade openness for economic growth by enabling firms to access credit and invest in new technologies. Their study highlighted that countries with well-functioning financial systems are better positioned to leverage the growth opportunities offered by global trade.

Koseet al. (2009) provided evidence from emerging markets showing that FDI inflows when combined with trade openness and financial development, lead to sustained economic growth. They argued that the positive effects of FDI are amplified in environments where financial institutions are capable of managing capital inflows effectively.

Blomstrom and Kokko (1997) examined how trade reforms impact the ability of countries to attract FDI, noting that reducing trade barriers creates a more favorable environment for foreign investors. Their study emphasized the importance of consistent trade policies to ensure that the benefits of FDI are fully realized.

Disyatat and Borio (2011) highlighted the role of FDI as a stable source of external financing for developing economies, contrasting it with the more volatile nature of portfolio investments. They found that FDI tends to be less susceptible to sudden reversals, making it a crucial component of long-term growth strategies.

Nath and Liu (2017) examined the role of information and communication technology (ICT) in enhancing the positive effects of FDI on economic growth. Their study found that countries with better ICT infrastructure were able to maximize the growth potential of FDI by improving productivity and fostering innovation.

Method

This study employs annual data spanning from 1999 to 2023 to explore the interplay between foreign direct investment (FDI), trade openness, and financial development in driving economic growth in Bangladesh. To address potential econometric issues such as heteroscedasticity, natural logarithms of all variables have been taken. This transformation is employed to stabilize the variance across the data points, enhancing the reliability of the regression estimates. All data are sourced from the World Bank’s World Development Indicators (WDI). This dataset forms the basis for exploring the relationships among the studied variables through econometric modeling. The variables included in the study are summarized in Table I.

Table I. Description of Variables

Variable	Description	Source
GDP per capita (ln_GDPpercapita)	GDP per capita at constant 2015 US dollars. Represents the average income per person in Bangladesh. Used as a measure of economic growth.	World Bank, World Development Indicators (WDI)
Foreign Direct Investment (ln_FDI)	Net inflows of FDI as a percentage of GDP. Measures the investment from foreign entities in Bangladesh’s economy.	World Bank, World Development Indicators (WDI)
Trade Openness (ln_Trade)	The sum of exports and imports of goods and services as a percentage of GDP. Indicates how open Bangladesh is to international trade.	World Bank, World Development Indicators (WDI)
Financial Development (ln_DomesticCredit)	Domestic credit to the private sector as a percentage of GDP. Represents the availability of credit for businesses and individuals from financial institutions in Bangladesh.	World Bank, World Development Indicators (WDI)

To analyze the relationships between foreign direct investment (FDI), trade openness, financial development, and economic growth in Bangladesh, this study employs a Vector Error Correction Model (VECM). The VECM framework is appropriate given the presence of cointegration among the variables, which indicates a long-run equilibrium relationship despite potential short-run deviations (Johansen, 1991). This model enables the investigation of both short-term and long-term relationships, capturing how variables adjust to equilibrium after a shock.

Testing their order of integration is essential to ensure that the variables are suitable for cointegration analysis. The order of integration indicates the number of times a variable must be differenced to achieve stationarity. Stationarity means that the statistical properties of the series, such as mean and variance, are constant over time, which is a key assumption for many time series models, including the Vector Error Correction Model (VECM). The Augmented Dickey-Fuller (ADF) test and the Phillips-Perron (PP) test are two widely used tests for checking the stationarity of time series. The key difference between the PP test and the ADF test is that the PP test makes corrections to the test statistic without adding lagged differenced terms.

The ADF test checks for the presence of a unit root, which indicates non-stationarity in the time series data. The ADF test is an extension of the simple Dickey-Fuller test that includes lagged differences of the dependent variable to account for higher-order serial correlation (Dickey & Fuller, 1979). The test equation is specified as follows:

(1) $$\begin{array}{r}\mathrm{\Delta }{Y}_t=\alpha +{\beta }_t+\mathrm{\gamma }{Y}_{t-1}+\sum _{i=1}^p{\delta }_i\mathrm{\Delta }{Y}_{t-i}+{ϵ}_t\end{array}$$

where:

$\mathrm{\Delta }{Y}_t$ is the first difference of the time series $Y_t$,

α is the intercept (constant term),

$\beta t$ represents a time trend,

$\gamma Y_{t-1}$ tests for the presence of a unit root, with $\gamma$ being the key coefficient of interest,

$\sum _{i=1}^p{\delta }_i\mathrm{\Delta }{Y}_{t-i}$ includes lagged first differences to account for autocorrelation,

${ϵ}_t$ is the error term.

The Phillips-Perron (PP) test is another method for testing the presence of a unit root in a time series. Unlike the ADF test, the PP test does not include lagged difference terms to account for autocorrelation (Phillips & Perron,1988). Instead, it adjusts the Dickey-Fuller test statistic using a non-parametric correction for serial correlation and heteroskedasticity in the error term. The test equation is:

(2) $$\begin{array}{r}\mathrm{\Delta }{Y}_t=\alpha +{\beta }_t+\gamma Y_{t-1}+{ϵ}_t\end{array}$$

The Johansen co-integration test determines whether long-run equilibrium relationships exist among the variables. The test evaluates the null hypothesis of no cointegration (no long-run relationship) against the alternative hypothesis that one or more cointegrating vectors exist among the variables (Johansen, 1991).

(3) $$\begin{array}{r}\text{Trace Statistic}=-T\sum _{i=r+1}^{n}ln(1-{\lambda }_i)\end{array}$$

where:

T is the number of observations,

λi represents the is the ith latest established correlation.

The null hypothesis is that there are at most r cointegrating relationships. A significant test statistic means rejecting the null hypothesis in favor of more cointegrating vectors.

(4) $$\begin{array}{r}\text{Max-Eigen Statistic}=-T\ln (1-{\lambda }_{r+1})\end{array}$$

The null hypothesis tests that there are exactly r cointegrating relationships against the alternative of r + 1. A significant test statistic suggests the existence of an additional cointegrating vector.

Given the cointegrating relationships, the VECM is specified to analyze how the variables adjust towards long-term equilibrium while capturing their short-term dynamics. The VECM for this study is represented as follows:

(5) $$\begin{array}{r}\mathrm{\Delta }{Y}_t=\mathrm{\Pi }{Y}_{t-1}+\sum _{i=1}^{p-1}{\mathrm{\Gamma }}_i\mathrm{\Delta }{Y}_{t-i}+u_t\end{array}$$

where:

$Y_t$ is a vector of the endogenous variables ln(GDP per capita), ln(FDI), ln(Trade Openness), ln(Domestic Credit).

Δ denotes the first difference operator,

Γi represents the short-term adjustment coefficients,

$u_t$ is the vector of error terms,

$p$ represents the selected number of lags.

Results and Discussion

Stationary Test

I conducted unit root tests to check the stationarity of variables using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. Our analysis focused on the logarithmic transformations of GDP per capita, foreign direct investment (FDI), Trade, and Domestic Credit. The results indicated that all variables, except Domestic Credit, are non-stationary at their levels but achieve stationarity upon first differencing (Table II). The null hypothesis of a unit root for GDP per capita, foreign direct investment (FDI), and trade openness could not be rejected at their levels. These variables become stationary when the first difference of these variables is taken. On the contrary, Domestic credit exhibits stationarity at the level, with ADF and PP p-values of 0.0471 and 0.0317, respectively.

Table II. Results of Unit Root Tests

Variable	Parameter	ADF test statistic	ADF P-value	PP test statistic	PP asymptotic P-value
ln_GDPpercapita	Level	2.701	0.999	2.426	0.999
	First diff.	−3.164	0.022	−3.073	0.029
ln_FDI	Level	−1.887	0.338	−1.873	0.345
	First diff.	−4.859	0	−4.870	0
ln_Trade	Level	−1.682	0.440	−1.738	0.411
	First diff.	−4.137	0.001	−4.090	0.001
ln_DomesticCredit	Level	−2.886	0.047	−3.036	0.032

Johansen Co-Integration Test

Following the unit root test of our variables, I conducted the Johansen cointegration test to explore the long-term relationships among GDP, Trade, Foreign Direct Investment, and Domestic Credit. The results are detailed in the accompanying table. The Trace and Max-Eigenvalue statistics significantly exceed their respective critical values at the 5% significance level, rejecting the null hypothesis of no cointegration (Table III). This confirms the presence of cointegration among the variables studied. Specifically, the Trace and Max-Eigenvalue tests indicate the existence of three cointegrating equations, suggesting robust long-term relationships among these economic variables. Therefore, our findings support the existence of significant and stable long-term equilibria that influence the dynamics between these economic measures.

Table III. Results of Johansen Co-Integration Test

Rank hypothesis	Trace statistic	0.05 Critical value	Max-eigen statistic	0.05 Critical value
None	126.8388	47.21	79.7672	27.07
At most 1	47.0716	29.68	23.3764	20.97
At most 2	23.6952	15.41	16.2008	14.07
At most 3	2.4944	3.76	2.4944	3.76

Optimum Lag Length

Our study selected the optimal lag length for the Vector Error Correction Model (VECM) using various statistical criteria derived from an unrestricted Vector Autoregressive (VAR) model. The model selection process was guided by the Final Prediction Error (FPE), Akaike Information Criterion (AIC), Hannan-Quinn Information Criterion (HQIC), and Schwarz Bayesian Information Criterion (SBIC). According to the results summarized in Table IV, Lag 4 significantly outperformed other models across multiple criteria, clearly marking it as the most suitable choice for our analysis.

Table IV. Results of Optimum Lag Length Selection

Lag	LL	LR	FPE	AIC	HQIC	SBIC
0	88.0819		2.60E-09	−8.40819	−8.36931	−8.20904
1	116.686	57.209	7.80E-10	−9.66862	−9.47425	−8.67289
2	123.933	14.494	2.40E-09	−8.79333	−8.44345	−7.00101
3	138.002	28.138	5.90E-09	−8.60024	−8.09486	−6.01134
4	540.357	804.71	9.3e-25*	−47.2357*	−46.5748*	−43.8502*

VECM Estimation

The Vector Error Correction Model (VECM) was applied to analyze the dynamic relationships among changes in GDP per capita, Foreign Direct Investment (FDI), trade openness, and domestic credit in Bangladesh. The model yielded significant insights into both short-term dynamics and long-term equilibrium relationships. The analysis demonstrated a robust error correction mechanism, especially in the equations for GDP and FDI (Table V). The model’s R-squared values indicated that the changes in GDP per capita and FDI could explain 92.18% and 97.84% of their variances, respectively. This suggests a strong predictive capability within the model for these variables. In contrast, the equations for trade and domestic credit were less robust but still meaningful, with R-squared values of 56.16% and 67.50%, respectively.

Table V. Results of Vector Error Correction Model (VECM)

Error Correction	D(ln_GDPpercapita)	D(ln_FDI)	D(ln_Trade)	(ln_DomesticCredit)
_ce1 (L1)	−2.3735	−80.1894	−8.4120	3.8604
	(0.6597)	(14.4963)	(19.4755)	(5.9076)
	[−3.60]***	[−5.53]***	[−0.43]	[0.65]
_ce2 (L1)	−0.0215	−4.5292	−0.1203	0.0002
	(0.0263)	(0.5786)	(0.7773)	(0.2358)
	[−0.82]	[−7.83]***	[−0.15]	[0.00]
_ce3 (L1)	0.0732	5.7193	0.4144	0.2902
	(0.0445)	(0.9779)	(1.3138)	(0.3985)
	[1.64]	[5.85]***	[0.32]	[0.73]
Short-Run Dynamics
LD.(ln_GDPpercapita)	1.2174	81.2705	6.0777	−3.9217
	(0.6798)	(14.9360)	(20.0661)	(6.0868)
	[1.79]	[5.44]***	[0.30]	[−0.64]
L2D.(ln_GDPpercapita)	0.9092	59.1485	−0.2908	−2.8165
	(0.5606)	(12.3174)	(16.5481)	(5.0196)
	[1.62]	[4.80]***	[−0.02]	[−0.56]
L3D.(ln_GDPpercapita)	0.6654	39.1269	1.5535	−0.3344
	(0.3908)	(8.5870)	(11.5365)	(3.4994)
	[1.70]	[4.56]***	[0.13]	[−0.10]
LD. ln_FDI	−0.0648	−5.5039	−0.8615	−0.1412
	(0.0443)	(0.9727)	(1.3068)	(0.3964)
	[−1.46]	[−5.66]***	[−0.66]	[−0.36]
L2D. ln_FDI	−0.0458	−3.5399	−0.6255	−0.2441
	(0.0358)	(0.7860)	(1.0560	(0.3203)
	[−1.28]	[−4.50]***	[−0.59]	[−0.76]
L3D. ln_FDI	−0.0388	−0.9211	−0.3086	−0.1305
	(0.0234)	(0.5152)	(0.6921)	(0.2099)
	[−1.66]	[−1.79]	[−0.45]	[−0.62]
LD. ln_Trade	−0.0070	−2.8875	0.3958	−0.0020
	(0.0820)	(2.5925)	(1.6835)	(0.6938)
	[−0.09]	[−1.11]	[0.24]	[−0.00]
L2D. ln_Trade	−0.0825	−3.9512	−0.1605	0.4178
	(0.0714)	(2.2573)	(1.4659)	(0.6041)
	[−1.16]	[−1.75]	[−0.11]	[0.69]
L3D. ln_Trade	−0.0974	−2.7612	0.2275	0.5132
	(0.0737)	(2.3311)	(1.5138)	(0.6239)
	[−1.32]	[−1.18]	[0.15]	[0.82]
L. ln_DomesticCredit	0.0018	3.6612	−0.1307	−0.3286
	(0.0568)	(1.2483)	(1.6770)	(0.5087)
	[0.03]	[2.93]***	[−0.08]	[−0.65]
L2. ln_DomesticCredit	−0.0380	0.2881	−0.9620	0.2048
	(0.0521)	(1.1443)	(1.5374)	(0.4663)
	[−0.73]	[0.25]	[−0.63]	[0.44]
L3. ln_DomesticCredit	−0.1221	−2.0278	−0.0221	0.3882
	(0.0614)	(1.3486)	(1.8118)	(0.5496)
	[−1.99]*	[−1.50]	[−0.01]	[0.71]
Constant	−0.0022	−0.000005	0.0006	−0.0002
	(0.0026)	(0.0263)	(0.0756)	(0.0229)
	[−0.87]	[0.00]	[0.01]	[−0.01]
R-squared	0.9218	0.9784	0.5616	0.675
S.E. of equation	0.0085	0.1871	0.2513	0.0762
F-statistic	47.1235	181.3128	5.1236	8.3062
Akaike AIC	−40.9286	−40.2774	−37.5929	−40.2774

In the short run, the coefficients of lagged differences indicate how previous values of GDP, FDI, trade, and domestic credit influence their current values. The first lag of GDP changes significantly predicted current GDP changes, indicating a strong persistence in GDP growth. This was also reflected in FDI, where the first lag significantly predicted current FDI changes, underlining FDI’s responsive adjustment to its previous levels.

The error correction terms, which capture the speed of adjustment back to the long-term equilibrium after short-term shocks, were particularly telling. For GDP, the coefficient of −2.3735 with a standard error of 0.6597 and highly significant at the 0.1% level suggested a relatively fast adjustment to equilibrium. In the case of FDI, the error correction term is highly significant, indicating a robust and swift response to deviations from its equilibrium path. This suggests that any shocks to FDI levels are quickly counteracted, highlighting the strong regulatory or market mechanisms in place that ensure FDI returns to stable levels efficiently.

Granger Causality Test

Based on the Granger Causality test results, several key relationships within the economic indicators of Bangladesh can be observed. The tests indicate that GDP per capita Granger causes changes in trade openness, suggesting that fluctuations in GDP per capita can predict variations in trade activities (Table VI). FDI and Domestic Credit Granger causes GDP which suggests that fluctuations in FDI and Domestic Credit can predict changes in GDP. The null hypothesis for the causal relationship from GDP per capita to FDI and to domestic credit cannot be rejected, indicating that GDP per capita does not significantly predict changes in these variables. Similarly, FDI does not show significant causative effects on trade openness, or domestic credit, implying limited influence of FDI on these aspects of the economy at the tested significance levels. Moreover, trade openness does not Granger cause changes in GDP, FDI, or domestic credit.

Table VI. Results of the Granger Causality Test

Null hypothesis	F-Statistic	Prob.
d_ln_GDPpercapita does not Granger Cause d_ln_FDI	1.3366	0.248
d_ln_GDPpercapita does not Granger Cause d_ln_Trade	3.9948	0.046
d_ln_GDPpercapita does not Granger Cause ln_DomesticCredit	2.3125	0.128
d_ln_FDI does not Granger Cause d_ln_GDPpercapita	0.0004	0.01
d_ln_FDI does not Granger Cause d_ln_Trade	0.04909	0.825
d_ln_FDI does not Granger Cause ln_DomesticCredit	12.011	0.984
d_ln_Trade does not Granger Cause d_ln_GDPpercapita	12.321	0.726
d_ln_Trade does not Granger Cause d_ln_FDI	0.52667	0.468
d_ln_Trade does not Granger Cause ln_DomesticCredit	0.98711	0.32
ln_DomesticCredit does not Granger Cause d_ln_GDPpercapita	0.0021	0.02
ln_DomesticCredit does not Granger Cause d_ln_FDI	0.17866	0.673
ln_DomesticCredit does not Granger Cause d_ln_Trade	0.88269	0.347

Diagnostic Tests

The statistical tests conducted on our dataset affirm the robustness of the econometric model employed in our analysis. The Jarque-Bera test for normality reports a statistic of 0.91 with a p-value of 0.633, indicating that the residuals of our model are normally distributed (Table VII). This supports the assumption necessary for the appropriate application of classical statistical tests in our analysis. To assess heteroskedasticity using the Breusch-Pagan-Godfrey method, we observe an R² of 1.56 and a p-value of 0.669. This suggests that there is no significant heteroskedasticity present in the model, affirming that the variance of the residuals does not vary with the level of the independent variables. Furthermore, the Breusch-Godfrey LM test for serial correlation yields an R² of 1.675 and a p-value of 0.795, indicating the absence of serial correlation in the model’s residuals. This finding ensures that the residuals are independent of each other, which is vital for the reliability of the regression coefficients. These test results are fundamental for validating the assumptions of our econometric model, ensuring that the estimations and inferences drawn from the model are based on solid statistical foundations.

Table VII. Results of the Diagnostic Tests

Test	Result
Normality Test (Jarque-Bera)
Jarque-Bera	0.91
p-value	0.633
Heteroskedasticity Test (Breusch-Pagan-Godfrey)
Observed R2	1.56
p-value	0.669
Serial Correlation Test (Breusch-Godfrey LM)
Observed R2	1.675
p-value	0.795

Conclusion

The research explores the dynamics between foreign direct investment (FDI), trade openness, financial development, and economic growth in Bangladesh, a rapidly developing South Asian economy. Through a Vector Error Correction Model (VECM), this study investigates the short-term effects and long-term relationships of these factors, which have been integral to Bangladesh’s economic expansion. The results highlight the significant role of FDI and trade openness in promoting economic growth, primarily by enhancing productivity and exports. Financial development also proves critical by improving resource allocation and supporting business investments. The analysis reveals that GDP growth significantly predicts changes in trade, suggesting that economic growth influences trade policies. Meanwhile, domestic credit shows a significant predictive relationship with GDP growth, emphasizing the financial sector’s impact on the broader economy. These findings underline the necessity for policy strategies that integrate investment, trade, and financial policies to ensure sustainable economic growth. The insights provided are valuable for policymakers aiming to optimize economic strategies and reforms in Bangladesh to maximize the benefits of FDI, increase trade openness, and strengthen financial development.